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Veys, L.

Publications and source records attributed to Veys, L..

2 recordsLinked to original sources

b-move: faster bidirectional character extensions in a run-length compressed index

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.s r-index and Nishimoto and Tabeis move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient bidirectional character extensions in run-length compressed space. It achieves bidirectional character extensions up to 8 times faster than the br-index, closing the performance gap with FM-index-based alternatives, while maintaining the br-indexs favorable memory characteristics. For example, all available complete E. coli genomes on NCBIs RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. Thus, b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license. FundingLore Depuydt: PhD Fellowship FR (1117322N), Research Foundation - Flanders (FWO) Luca Renders: PhD Fellowship SB (1SE7822N), Research Foundation - Flanders (FWO) Travis Gagie: NSERC Discovery Grant RGPIN-07185-2020 to Travis Gagie and NIH grant R01HG011392 to Ben Langmead

bioinformatics↗

The AppNL-G-F mouse retina is a site for preclinical Alzheimer's disease diagnosis and research

In this study, we report the results of a comprehensive phenotyping of the retina of the AppNL-G-F mouse. We demonstrate that soluble A{beta} accumulation is present in the retina of these mice early in life and progresses to A{beta} plaque formation by midlife. This rising A{beta} burden coincides with local microglia reactivity, astrogliosis, and abnormalities in retinal vein morphology. Electrophysiological recordings reveal signs of neuronal dysfunction yet no overt neurodegeneration was observed and visual performance outcomes were unaffected in the AppNL-G-F mouse. Furthermore, we show that hyperspectral imaging can be used to quantify retinal A{beta}, underscoring its potential as a biomarker for AD diagnosis and monitoring. These findings suggest that the AppNL-G-F retina mimics the early, preclinical stages of AD, and, together with retinal imaging techniques, offers unique opportunities for drug discovery and fundamental research into preclinical AD.

neuroscience↗